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Record W2076584857 · doi:10.1121/1.4784288

Stimulus continuity is not necessary for the salience of dynamic sound localization cues.

2009· article· en· W2076584857 on OpenAlexaff
Ewan A. Macpherson

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsWestern University
Fundersnot available
KeywordsStimulus (psychology)AcousticsSound localizationMathematicsSalience (neuroscience)PhysicsComputer sciencePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Correspondence between head rotation and resulting changes in interaural difference cues provides information about sound source location. We assessed whether source continuity or merely relative displacement is necessary for use of this dynamic localization cue. Low-frequency (0.5–1 kHz) noise-band targets, not correctly localizable in the absence of head motion or for motion duration <50 ms [Macpherson and Kerr, APCAM (2008)], were presented while the listener performed a practiced head rotation at 50 deg/s. The stimuli were either continuous (a single burst gated on and off as the head entered and exited a variable-width spatial window) or discrete (two 20 ms endpoint bursts, triggered as the head entered and exited the window). Human listeners reported the apparent location of the stimulus by orienting with their heads subsequent to the initial head rotation. The minimum head movement angle (MHMA) necessary to resolve front/rear ambiguity was measured for each stimulus type. Similar MHMAs of 5–10 deg were measured for continuous and discrete stimuli, suggesting that endpoint “snapshots'' providing only displacement information are sufficient for use of dynamic localization cues. That parallels the finding that stimulus continuity does not improve detection of source motion [Chandler and Grantham, J. Acoust. Soc. Am. 106, 1956–1968 (1992)]. [Work supported by NSF and NIH/NIDCD.]

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.251
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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